Autonomous Driving Comfort Prediction: Improve Comfort through Global Path Planning

Abstract

Abstract Comfort is a pivotal consideration in the realm of autonomous driving, prompting researchers to address methods of enhancing driving comfort for autonomous driving vehicles. A central focus of current research revolves around the judicious planning of driving trajectories with a primary emphasis on comfort. Given the transient nature of comfort, prevailing path planning strategies predominantly center on local considerations. Nevertheless, it is imperative to recognize that macroscopic factors, including traffic flow and road conditions, wield a substantial influence on comfort. For instance, complex traffic scenarios elevate the likelihood of emergency braking, thereby affecting comfort. Consequently, investigating the intricate interplay between comfort and global path planning becomes essential. In the context of autonomous driving, this paper introduces a methodology and framework for predicting driving comfort by leveraging road information. The study establishes a comprehensive road information-comfort dataset and devises predictive models for driving comfort, employing a multi-head attention approach. The ensuing discussion elucidates the practical application of the model in path selection and planning through illustrative examples. Finally, real-road vehicle tests were conducted. Following the utilization of the path optimized by the model, the autonomous driving car exhibited an approximate 17.3% reduction in the mean longitudinal jerk and a 9.7% reduction in the mean lateral jerk. These results serve to validate the effectiveness and significance of the proposed model. Notably, this research marks the pioneering integration of driving comfort with global path planning, which is of immense importance for autonomous driving navigation systems, offering an insightful and valuable reference for related study.